DOCSLIB.ORG

Tipping Points in Macroeconomic Agent-Based Models

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Tipping Points in Macroeconomic Agent-Based Models Tipping points in macroeconomic Agent-Based models Stanislao Gualdi,1 Marco Tarzia,2 Francesco Zamponi,3 and Jean-Philippe Bouchaud4 1 Universit´ePierre et Marie Curie - Paris 6, Laboratoire de Physique Th´eoriquede la Mati`ere Condens´ee, 4, Place Jussieu, Tour 12, 75252 Paris Cedex 05, France and IRAMIS, CEA-Saclay, 91191 Gif sur Yvette Cedex, France ∗ 2 Universit´ePierre et Marie Curie - Paris 6, Laboratoire de Physique Th´eoriquede la Mati`ere Condens´ee, 4, Place Jussieu, Tour 12, 75252 Paris Cedex 05, France 3Laboratoire de Physique Th´eorique, Ecole´ Normale Sup´erieure, UMR 8549 CNRS, 24 Rue Lhomond, 75231 Paris Cedex 05, France 4CFM, 23 rue de l'Universit´e,75007 Paris, France, and Ecole Polytechnique, 91120 Palaiseau, France. The aim of this work is to explore the possible types of phenomena that simple macroeconomic Agent-Based models (ABM) can reproduce. Our motivation is to understand the large macro- economic fluctuations observed in the \Mark I" ABM devised by D. Delli Gatti and collaborators. Our major finding is the existence of a first order (discontinuous) phase transition between a \good economy" where unemployment is low, and a \bad economy" where unemployment is high. We show that this transition is robust against many modifications of the model, and is induced by an asymmetry between the rate of hiring and the rate of firing of the firms. This asymmetry is induced, in Mark I, by the interest rate. As the interest rate increases, the firms become more and more reluctant to take further loans. The unemployment level remains small until a tipping point beyond which the economy suddenly collapses. If the parameters are such that the system is close to this transition, any small fluctuation is amplified as the system jumps between the two equilibria. We have also explored several natural extensions. One is to allow this hiring/firing propensity to depend on the financial fragility of firms. Quite interestingly, we find that in this case, the above transition survives but becomes second order. We also studied simple wage policies and confidence feedback effects, whereby higher unemployment increases the saving propensity of households. We observe several interesting effects, such as the appearance of acute endogenous crises, during which the unemployment rate shoots up before the economy recovers. We end the paper with general comments on the usefulness of ABMs to model macroeconomic phenomena, in particular in view of the time needed to reach a steady state. It is human nature to think wisely and to act absurdly { Anatole France I. INTRODUCTION: FROM MICRO-RULES TO MACRO-BEHAVIOUR Inferring the behaviour of large assemblies from the behaviour of its elementary constituents is arguably one of the most important problems in a host of different disciplines: physics, material sciences, biology, computer sciences, sociology and, of course, economics and finance. It is also a notoriously hard problem; statistical physics has developed in last 150 years essentially to understand this micro-macro link. Clearly, when interactions are absent or small enough, the system as a whole merely reflects the properties of individual entities. This is the canvas of traditional macro- economic approaches. Economic agents are assumed to be identical, non-interacting, rational agents, an idealization known as the \Representative Agent" (RA). In this framework, micro and macro trivially coincide. However, we know (in particular from physics) that discreteness, heterogeneities and/or interactions can lead to totally unexpected phenomena. Think for example of super-conductivity or super-fluidity: before their experimental discovery, it was simply beyond human imagination that individual electrons or atoms could \conspire" to create a collective state that can flow without friction. Micro and macro behaviour not only do not coincide in general, but genuinely surprising behaviour can emerge through aggregation. From the point of view of economic theory, this is interesting, because financial and economic history is strewn with bubbles, crashes, crises and upheavals of all sorts. These are very hard to fathom within a Representative Agent framework [1], within which crises would require large aggregate shocks, when in fact small local shocks can trigger large systemic effects when heterogeneities, interactions and network effects are taken into account [2, 3]. The need to include these effects has spurred a large activity in \Agent-Based models" ∗Electronic address: [email protected] 2 (ABMs) [4{6]. These models need numerical simulations, but are extremely versatile because any possible behavioural rules, interactions, heterogeneities can be taken into account. In fact, these models are so versatile that they suffer from the \wilderness of high dimensional spaces" (paraphrasing Sims [7]). The number of parameters and explicit or implicit choices of behavioural rules is so large (∼ 10 or more, even in the simplest models, see below) that the results of the model may appear unreliable and arbitrary, and the calibration of the parameters is an hopeless (or highly unstable) task. Mainstream RA models, on the other hand, are simple enough to lead to closed form analytical results, with simple narratives and well trodden calibration avenues. In spite of their unrealistic character, these traditional models are still favoured by most economists, both in academia and in institutional and professional circles. ABMs are seen at best as a promising research direction and at worst as an unwarranted \black box" (see [8] for an enlightening discussion on the debate between traditional DSGE models and ABMs, and [9, 10] for further insights). At this stage, it seems to us that some clarifications are indeed needed, concerning both the objectives and method- ology of Agent-Based models. ABMs do indeed suffer from the wilderness of high dimensional spaces, and some guidance is necessary to put these models on a firm footing. In this respect, statistical physics offers a key concept: the phase diagram in parameter space [11]. A classic example, shown in Fig. 1, is the phase diagram of usual substances as a function of two parameters, here temperature and pressure. The generic picture is that the number of distinct phases is usually small (e.g. three in the example of Fig. 1: solid, liquid, gas). Well within each phase, the properties are qualitatively similar and small changes of parameters have a small effect. Macroscopic (aggregate) properties do not fluctuate any more for very large systems and are robust against changes of microscopic details. This is the \nice" scenario, where the dynamics of the system can be described in terms of a small number of macroscopic (aggregate) variables, with some “effective" parameters that encode the microscopic details. Other scenarios are of course possible; for example, if one sits close to the boundary between two phases, fluctuations can remain large even for large systems and small changes of parameters can radically change the macroscopic behaviour of the system. There may be mechanisms naturally driving the system close to criticality (like in Self Organized Criticality [12]), or, alternatively, situations in which whole phases are critical, like for \spin-glasses" [13]. In any case, the very first step in exploring the properties of an Agent-Based model should be, we believe, to identify the different possible phases in parameter space and the location of the phase boundaries. In order to do this, numerical simulations turn out to be very helpful [14, 15]: aggregate behaviour usually quickly sets in, even for small sizes. Some parameters usually turn out to be crucial, while others are found to play little role. This is useful to establish a qualitative phenomenology of the model { what kind of behaviour can the model reproduce, which basic mechanisms are important, which effects are potentially missing. This first, qualitative step allows one to unveil the \skeleton" of the ABM. Simplified models that retain most of the phenomenology can then be constructed and perhaps solved analytically, enhancing the understanding of the important mechanisms, and providing some narrative to make sense of the observed effects. In our opinion, calibration of an ABM using real data can only start to make sense after this initial qualitative investigation is in full command { which is in itself not trivial when the number of parameters is large. The phase diagram of the model allows one to restrict the region of parameters that lead to \reasonable" outcomes (see for example the discussion in [16, 17]). The aim of this paper is to put these ideas into practice in the context of a well-studied macroeconomic Agent-Based model (called \Mark I" below), devised by Delli Gatti and collaborators [18, 19]. This model is at the core of the European project \CRISIS", which justifies our attempt to shed some theoretical light on this framework.1 In the first part of the paper, we briefly recall the main ingredients of the model and show that as one increases the baseline interest rate, there is a phase transition between a \good" state of the economy, where unemployment is low and a \bad" state of the economy where production and demand collapse. In the second part of the paper, we propose a highly simplified version of Mark I that aims at capturing the main drivers of this phase transition. We show that the most important parameter is the asymmetry between the firms’ propensity to hire (when business is good) or to fire (when business is bad). In Mark I, this asymmetry is induced by the reaction of firms to the level of interest rates, but other plausible mechanisms would lead to the same effect. The simplest version of the model is amenable to an analytic treatment and exhibits a \tipping point" (i.e. a discontinuous transition) between high employment and high unemployment. Enriching the model by, e.g. endowing firms with a strategy that depends on their financial wealth, leads to a modified picture, where the level of unemployment is now continuous, and where low frequency fluctuations of the employment rate/production appear due to the proximity of a second order transition.
Load more

Recommended publications
  • The Role of Values of Economists and Economic Agents in Economics: a Necessary Distinction Nestor Nieto
    The Role of Values of Economists and Economic Agents in Economics: A Necessary Distinction Nestor Nieto To cite this version: Nestor Nieto. The Role of Values of Economists and Economic Agents in Economics: A Necessary Distinction. 2021. hal-02735869v2 HAL Id: hal-02735869 https://hal.archives-ouvertes.fr/hal-02735869v2 Preprint submitted on 28 May 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Copyright The Role of Values of Economists and Economic Agents in Economics: A Necessary Distinction Nestor Lovera Nieto1 Abstract The distinction between the value judgments of economists and those of economic agents is not clear in the literature of welfare economics. In this article, I show that this distinction is important because economists have to step back and consider not only their own value judgments but also those of economic agents when they study economic phenomena. I use the subject of Interpersonal Comparisons of Utility to discuss this distinction, more specifically the analysis of Harsanyi’s impartial observer theorem, which provides a framework to justify that value judgments of economists and those of economic agents have to be properly identified. I suggest that it is essential to have a theoretical framework to make this distinction.
    [Show full text]
  • Agent-Based Macroeconomics
    Faculty of Business Administration and Economics Working Papers in Economics and Management No. 02-2018 January 2018 Agent-Based Macroeconomics H. Dawid D. Delli Gatti Bielefeld University P.O. Box 10 01 31 33501 Bielefeld − Germany ISSN 2196−2723 www.wiwi.uni−bielefeld.de Agent-Based Macroeconomics Herbert Dawid∗ Domenico Delli Gatti yz January 2018 This paper has been prepared as a chapter in the Handbook of Computational Economics, Volume IV, edited by Cars Hommes and Blake LeBaron. Abstract This chapter surveys work dedicated to macroeconomic analysis using an agent- based modeling approach. After a short review of the origins and general characteristics of this approach a systemic comparison of the structure and modeling assumptions of a set of important (families of) agent-based macroeconomic models is provided. The comparison highlights substantial similarities between the different models, thereby identifying what could be considered an emerging common core of macroeconomic agent-based modeling. In the second part of the chapter agent-based macroeconomic research in different domains of economic policy is reviewed. Keywords: Agent-based Macroeconomics, Aggregation, Heterogeneity, Behavioral Rules, Business Fluctuations, Economic Policy JEL Classification: C63, E17, E32, E70, 1 Introduction Starting from the early years of the availability of digital computers, the analysis of macroe- conomic phenomena through the simulation of appropriate micro-founded models of the economy has been seen as a promising approach for Economic research. In an article in the American Economic Review in 1959 Herbert Simon argued that "The very complexity that has made a theory of the decision-making process essential has made its construction exceedingly difficult.
    [Show full text]
  • Alternative Microfoundations for Strategic Classification
    Alternative Microfoundations for Strategic Classification Meena Jagadeesan 1 Celestine Mendler-Dünner 1 Moritz Hardt 1 Abstract the context of macroeconomic policy making fueled the microfoundations program following the influential critique When reasoning about strategic behavior in a ma- of macroeconomics by Lucas in the 1970s (Lucas Jr, 1976). chine learning context it is tempting to combine Microfoundations refers to a vast theoretical project that standard microfoundations of rational agents with aims to ground theories of aggregate outcomes and popula- the statistical decision theory underlying classifi- tion forecasts in microeconomic assumptions about individ- cation. In this work, we argue that a direct combi- ual behavior. Oversimplifying a broad endeavor, the hope nation of these ingredients leads to brittle solution was that if economic policy were microfounded, it would concepts of limited descriptive and prescriptive better anticipate the response that the policy induces. value. First, we show that rational agents with perfect information produce discontinuities in the Predominant in neoclassical economic theory is the as- aggregate response to a decision rule that we of- sumption of an agent that exhaustively maximizes a util- ten do not observe empirically. Second, when any ity function on the basis of perfectly accurate informa- positive fraction of agents is not perfectly strate- tion. This modeling assumption about agent behavior under- gic, desirable stable points—where the classifier writes many celebrated results on markets, mechanisms, and is optimal for the data it entails—no longer exist. games. Although called into question by behavioral eco- Third, optimal decision rules under standard mi- nomics and related fields (e.g.
    [Show full text]
  • The 2001 Trading Agent Competition
    From: AAAI-02 Proceedings. Copyright © 2002, AAAI (www.aaai.org). All rights reserved. The 2001 Trading Agent Competition Michael P. Wellman∗ Amy Greenwald Peter Stone Peter R. Wurman University of Michigan Brown University AT&T Labs — Research North Carolina State University [email protected] [email protected] [email protected] [email protected] Abstract to useful observations, but all conclusions from such investi- gations must be qualified by questions of realism. Since the The 2001 Trading Agent Competition was the second in a effectiveness of one agent’s strategy depends on the strate- series of events aiming to shed light on research issues in gies of others, having one designer choose all strategies in- automating trading strategies. Based on a challenging mar- ket scenario in the domain of travel shopping, the compe- troduces a great source of fragility to the research exercise. tition presents agents with difficult issues in bidding strat- One natural approach is for researchers to cooperate, by egy, market prediction, and resource allocation. Entrants in addressing their design energy to a common problem. The 2001 demonstrated substantial progress over the prior year, Trading Agent Competition (TAC) is an attempt to induce with the overall level of competence exhibited suggesting that this cooperation, by organizing an event providing infras- trading in online markets is a viable domain for highly au- tructure, and promising external attention to the results. tonomous agents. The first TAC (Wellman et al. 2001) was held in July 2000 in Boston, in conjunction with the International Con- ference on Multiagent Systems (ICMAS-00).
    [Show full text]
  • Chapter 3 the Representative Agent Model
    Chapter 3 The representative agent model 3.1 Optimal growth In this course we’re looking at three types of model: 1. Descriptive growth model (Solow model): mechanical, shows the im- plications of a given fixed savings rate. 2. Optimal growth model (Ramsey model): pick the savings rate that maximizes some social planner’s problem. 3. Equilibrium growth model: specify complete economic environment, find equilibrium prices and quantities. Two basic demographies - rep- resentative agent (RA) and overlapping generations (OLG). 3.1.1 The optimal growth model in discrete time Time and demography Time is discrete. One representative consumer with infinite lifetime. Social planner directly allocates resources to maximize consumer’s lifetime expected utility. 1 2 CHAPTER 3. THE REPRESENTATIVE AGENT MODEL Consumer The consumer has (expected) utility function: ∞ X t U = E0 β u(ct) (3.1) t=0 where β ∈ (0, 1) and the flow utility or one-period utility function is strictly increasing (u0(c) > 0) and strictly concave (u00(c) < 0). In addition we will impose an Inada-like condition: lim u0(c) = ∞ (3.2) c→0 The social planner picks a nonnegative sequence (or more generally a stochas- tic process) for ct that maximizes expected utility subject to a set of con- straints. The technological constraints are identical to those in the Solow model. Specifically, the economy starts out with k0 units of capital at time zero. Output is produced using labor and capital with the standard neoclassical production function. Labor and technology are assumed to be constant, and are normalized to one. yt = F (kt,Lt) = F (kt, 1) (3.3) Technology and labor force growth can be accommodated in this model by applying the previous trick of restating everything in terms of “effective la- bor.” Output can be freely converted to consumption ct, or capital investment xt, as needed: ct + xt ≤ yt (3.4) Capital accumulation is as in the Solow model: kt+1 = (1 − δ)kt + xt (3.5) Finally, the capital stock cannot fall below zero.
    [Show full text]
  • Agent-Based Models and Economic Policy
    AGENT-BASED MODELS AND ECONOMIC POLICY edited by Jean-Luc Gaffard and Mauro Napoletano AGENT-BASED MODELS AND ECONOMIC POLICY f Revue de l’OFCE / Debates and policies OFCE L’Observatoire français des conjonctures économiques est un organisme indépendant de prévision, de recherche et d’évaluation des politiques publiques. Créé par une convention passée entre l'État et la Fondation nationale des sciences politiques approuvée par le décret n° 81.175 du 11 février 1981, l'OFCE regroupe plus de 40 chercheurs français et étrangers, auxquels s’associent plusieurs Research fellows de renommée internationale (dont trois prix Nobel). « Mettre au service du débat public en économie les fruits de la rigueur scientifique et de l’indépendance universitaire », telle est la mission que l’OFCE remplit en conduisant des travaux théoriques et empiriques, en participant aux réseaux scientifiques internationaux, en assurant une présence régulière dans les médias et en coopérant étroitement avec les pouvoirs publics français et européens. Philippe Weil préside l’OFCE depuis 2011, à la suite de Jean-Paul Fitoussi, qui a succédé en 1989 au fondateur de l'OFCE, Jean-Marcel Jeanneney. Le président de l'OFCE est assisté d'un conseil scientifique qui délibère sur l'orientation de ses travaux et l'utilisation des moyens. Président Philippe Weil Direction Estelle Frisquet, Jean-Luc Gaffard, Jacques Le Cacheux, Henri Sterdyniak, Xavier Timbeau Comité de rédaction Christophe Blot, Jérôme Creel, Estelle Frisquet, Gérard Cornilleau, Jean-Luc Gaffard, Éric Heyer, Éloi Laurent, Jacques Le Cacheux, Françoise Milewski, Lionel Nesta, Hélène Périvier, Mathieu Plane, Henri Sterdyniak, Xavier Timbeau Publication Philippe Weil (directeur de la publication), Gérard Cornilleau (rédacteur en chef), Laurence Duboys Fresney (secrétaire de rédaction), Najette Moummi (responsable de la fabrication) Contact OFCE, 69 quai d’Orsay 75340 Paris cedex 07 Tel.
    [Show full text]
  • Simplified Glossary of Agricultural Economics Terms And
    A SIMPtlPIFT! -"'/^ 'iARY OF AOR^'"'"'"*f> K00!fOMXC3 TIRMS AKB cowErrs fh ?ly usRn ik oai,3 rbao by kahsavs WITR A t)l5CTJS3I0ll OF THE WRID FOH SUCH A GLOSSARY by ROBBRf imSSSLti 30Hm A* B*, WMhlwni ilnnloipal Unlveralty of Top^a, 1949 A THESIS ottlgBSltted 1b partial fulfllliaaiit of th« roqylre^onts for tha dograe iiJH J A :. I V ' ..CIERCS OapartMMt of Taebaloal JomwtmXttm ITY 1959 Thi jfKXSem AsMPieftn oconoaty Is an exponsiv* meohftniam of irameT>ous oomponents, each with Its manifold facets and countless ramifioations* So colossal and complex a system requires of the citizen substantial resources of education and knowledge for responsible and Intelligent participation. Economics concerns us all* It plays a dominant role in our affairs, affecting our lives at every hour* All of us alike, workers and aianagers, producers md consumers, businessmen and investors, need to be equip|Md to function effectively in this economic society. The purposes of economic education may properly be to provide the individual with such a grasp of basie eoncepts and principles as will enable hln to understand, appreciate, and seek to Improve that economy the benefits of which he sheres, to vote intelligently on economic questions, and to use his knowledge for his own and for social good< Prom a purely selfish point of view, everyone s^uU seek a re8peet« able competence In economic information and skills* On the other hand, society can ill afford to neglect the provision of effective •eonomlc education, which is indispensable for orotecting our political
    [Show full text]
  • Agent-Based Modeling in Economics and Finance: Past, Present, and Future∇
    Agent-Based Modeling in Economics and Finance: Past, Present, and Future∇ Robert L. Axtell*& and J. Doyne Farmer&^ *Computational Social Science Ph.D. Program Departments of Economics, Computer Science, and Computational and Data Sciences Center for Social Complexity, Krasnow Institute for Advanced Study George Mason University 4400 University Drive Fairfax, VA 22030 USA &Santa Fe Institute 1399 Hyde Park Road Santa Fe, NM 87501 USA ^Complexity Economics Program INET@Oxford Mathematical Institute and Oxford Martin School Eagle House University of Oxford Walton Well Road Oxford OX2 6ED UK Abstract Agent-based modeling is a novel computational methodology for representing the behavior of individuals in order to study social phenomena. The approach is now exponentially growing in many fields. The history of its use in economics and finance is reviewed and disparate motivations for employing it are described. We highlight the ways agent modeling may be used to relax conventional assumptions in economic models. Typical approaches to calibrating and estimating agent models are surveyed. Software tools for agent modeling are assessed. Special problems associated with large-scale and data-intensive agent models are discussed. Our conclusions emphasize the strengths of this new approach while highlighting several challenges and important open problems, both conceptual and technological, which may usefully serve as milestones for future progress in agent computing within economics and finance, specifically, and the social sciences more broadly. JEL codes: C00, C63, C69, D00, E00, G00 Keywords: agent-based computational economics, multi-agent systems, agent-based modeling and simulation, distributed systems Version 1.09: 1 August 2018 ∇ This work was begun while RLA was visiting Oxford.
    [Show full text]
  • Agent Based-Stock Flow Consistent Macroeconomics: Towardsa Benchmark Model
    Agent Based-Stock Flow Consistent Macroeconomics: Towardsa Benchmark Model Alessandro Caiani∗ Antoine Godin Eugenio Caverzasi Marche Polytechnic University Kingston University Marche Polytechnic University Mauro Gallegati Stephen Kinsella Joseph E. Stiglitz Marche Polytechnic University University of Limerick Columbia University June 6, 2016 Abstract The paper moves from a discussion of the challenges posed by the crisis to standard macroeconomics and the solutions adopted within the DSGE community. Although several recent improvements have enhanced the realism of standard models, we argue that major drawbacks still undermine their reli- ability. In particular, DSGE models still fail to recognize the complex adaptive nature of economic systems, and the implications of money endogeneity. The paper argues that a coherent and exhaus- tive representation of the inter-linkages between the real and financial sides of the economy should be a pivotal feature of every macroeconomic model and proposes a macroeconomic framework based on the combination of the Agent Based and Stock Flow Consistent approaches. The papers aims at contributing to the nascent AB-SFC literature under two fundamental respects: first, we develop a fully-decentralized AB-SFC model with several innovative features, and we thoroughly validate it in order to check whether the model is a good candidate for policy analysis applications. Results suggest that the properties of the model match many empirical regularities, ranking among the best performers in the related literature, and that these properties are robust across different parameteri- zations. Second, the paper has also a methodological purpose in that we try to provide a set or rules and tools to build, calibrate, validate, and display AB-SFC models.
    [Show full text]
  • Agent-Based Computational Economics: a Constructive
    AGENT-BASED COMPUTATIONAL ECONOMICS: ∗ A CONSTRUCTIVE APPROACH TO ECONOMIC THEORY LEIGH TESFATSION Economics Department, Iowa State University, Ames, IA 50011-1070 18 December 2005 Contents Abstract Keywords 1. Introduction 2. ACE study of economic systems 3. From Walrasian equilibrium to ACE trading 3.1 Walrasian bliss in a hash-and-beans economy 3.2 Plucking out the Walrasian Auctioneer 3.3 The ACE Trading World: Outline 3.4 Defining “equilibrium” for the ACE Trading World 4. ACE modeling of procurement processes 4.1 Constructive understanding 4.2 The essential primacy of survival 4.3 Strategic rivalry and market power 4.4 Behavioral uncertainty and learning 4.5 The role of conventions and organizations 4.6 Interactions among attributes, institutions, and behaviors 5. Concluding remarks ∗Forthcoming in L. Tesfatsion and K. L. Judd (editors), Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland, to appear. Earlier versions of this study have been presented in the Computations in Science Distinguished Lecture Series (University of Chicago, October 2003), at the Post Walrasian Macroeconomics Conference (Middlebury College, May 2004), at the ACE Workshop (University of Michigan, May 2004), and in a Fall 2004 faculty seminar at ISU. For helpful conversations on the topics covered in this paper, thanks in particular to Bob Axelrod, Dave Batten, Facundo Bromberg, Myong-Hun Chang, Dave Colander, Chris Cook, Catherine Dibble, Josh Epstein, Charlie Gieseler, Joe Harrington,
    [Show full text]
  • The Principal-Agent Matching Market
    A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Dam, Kaniska Working Paper The Principal-Agent Matching Market CESifo Working Paper, No. 945 Provided in Cooperation with: Ifo Institute – Leibniz Institute for Economic Research at the University of Munich Suggested Citation: Dam, Kaniska (2003) : The Principal-Agent Matching Market, CESifo Working Paper, No. 945, Center for Economic Studies and ifo Institute (CESifo), Munich This Version is available at: http://hdl.handle.net/10419/76350 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu THE PRINCIPAL-AGENT MATCHING MARKET KANISKA DAM DAVID PÉREZ-CASTRILLO CESIFO WORKING PAPER NO. 945 CATEGORY 9: INDUSTRIAL ORGANISATION MAY 2003 Presented at Area Conference on Industrial Organisation, March 2003 An electronic version of the paper may be downloaded • from the SSRN website: www.SSRN.com • from the CESifo website: www.CESifo.de CESifo Working Paper No.
    [Show full text]
  • Monetary Policy with Heterogeneous Agents: Insights from TANK Models ∗
    Monetary Policy with Heterogeneous Agents: Insights from TANK models ∗ Davide Debortoli Jordi Galí UPF, CREI and Barcelona GSE CREI, UPF and Barcelona GSE September 2017 Preliminary and Incomplete Abstract Heterogeneous agents New Keynesian (HANK) models are shown to differ from their repre- sentative agent (RANK) counterparts along two dimensions: differences in average consump- tion at any point in time between constrained and unconstrained households, and consumption heterogeneity within the subset of unconstrained households. These two factors are captured in a simple way by two "wedges" that appear in an aggregate Euler equation, and whose behavior can be traced in response to any aggregate shock, allowing us to assess their quanti- tative significance. A simple two-agent New Keynesian (TANK) model abstracts completely from heterogeneity within unconstrained agents, but is shown to capture reasonably well the implications of a baseline HANK model regarding the effects of aggregate shocks on aggregate variables. We discuss the implications of our findings for the design of optimal monetary policy. JEL Classification Numbers: E21, E32, E50. Keywords: heterogeneous agents, new keynesian models, monetary policy. ∗We thank Cristina Manea for excellent research assistance. We have benefitted from comments by participants at CREI Macro Lunch, and 2017 NBER Summer Institute. Davide Debortoli acknowledges financial support from the European Union’s Horizon 2020 Program under grant agreement 649396 (ADEMU). 1 1 Introduction A growing literature has emerged in recent years that aims at re-examining some important macro questions through the lens of monetary models with heterogenous agents. Models in this literature commonly assume the presence of idiosyncratic shocks to individuals’ income, together with the existence of incomplete markets and borrowing constraints.
    [Show full text]